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Results from the THORPEX Observation Impact Inter-comparison Project

This study compares the impacts of observations in three forecast systems (NRL, GMAO, EC) using data denial and adjoint-based methods. Results show the largest impacts are from AMSU-A, raobs, satwind, and aircraft observations. The impacts vary depending on the system and observation type.

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Results from the THORPEX Observation Impact Inter-comparison Project

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  1. Results from the THORPEX Observation Impact Inter-comparison Project Ron Gelaro1, Rolf Langland2, Simon Pellerin3, Ricardo Todling1 1NASA Global Modeling and Assimilation Office (GMAO) 2Naval Research Laboratory (NRL) 3Environment Canada (EC) Third THORPEX International Science Symposium Sept 2009 – Monterey, CA

  2. Background / Motivation • A goal of THORPEX is to improve our understanding of the ‘value’ of observations provided by the current global network • optimize the use of current observations • inform the design, deployment of new obs systems • DAOS WG proposed, as a starting point, a comparison of observation impacts in several forecast systems, facilitated by the emergence of new (adjoint-based) techniques • Experiments for a ‘baseline’ observation set were designed by NRL, NASA/GMAO, EC, ECMWF and Météo France • …this talk presents results from 3 systems: NRL, GMAO, EC Acknowledgements: Carla Cardinali (ECMWF), Pierre Gauthier (EC, UQAM), Florence Rabier (Météo France)

  3. The Challenge With millions of observations assimilated every analysis cycle, how do we quantify the value provided by these data? • QUESTIONS: • How similar or different are the impacts of observations in one forecast system vs. another? • Which observation types have the largest total impacts, and impacts per-observation? • How do observation impacts vary as a function of location, channel, or other relevant attribute?

  4. Methods for Evaluating Observation Impact • DATA DENIAL / ADDITION EXPERIMENTS (OSEs) • Typically done for a few sets of observation types • Not feasible to examine impact of ALL observations • Valid for any forecast range or measure ADJOINT-BASED OBSERVATION IMPACT • Can evaluate impacts of all observations on a selected measure of short-range forecast error • Computationally efficient and accurate, but subject to assumptions and limitations in adjoint model • Method described in Langland and Baker (Tellus 2004) and subsequent papers by Errico 2007; Gelaro et al. 2007 ◄ Methods compared by Gelaro and Zhu (2009), Cardinali (2009)

  5. Definition of Observation Impact Energy-weighted forecast error norms Observation impact is quantified as the difference in forecast error norms It may be estimated as a sum of contributions from individual obs using information from the model and analysis adjoints observations assimilated   t 6h t = 0 t + 24h Assimilation of observations moves the model state from the background trajectory to the new analysis trajectory

  6. Calculation of Observation Impact • Based on LB04, or an extension of that method adjoint analysis scheme innovations where adjoint model • Impact computed for all observations simultaneously • The impact of any subset of observations, S, can be easily quantified using a partial sum

  7. Experiment for the Baseline Observation Set • January 2007, every 6 hours, 124 assimilation times • Baseline set of observations assimilated (next slide) • 24h global forecast error - Dry total energy norm • Dry physics in adjoint model • NRL – NOGAPS forecast and adjoint T239L30 NAVDAS 3D-Var analysis and adjoint 0.5° • GMAO – GEOS-5 forecast 0.5°, adjoint 1.0° GSI 3D-Var analysis and adjoint 0.5° • EC – Meso-Strat GFS forecast 0.4°, adjoint 1.5° 4D-Var analysis and adjoint 1.5°

  8. Observations in Baseline Comparison • Conventional observations • Raobs, pibals, dropsondes • Commercial aircraft • Land surface • Ship surface • Buoys • Satellite observations • AMSU-A (3 satellites) • Geo-satellite winds • Vis, IR and WV • MODIS polar winds • SSM/I surface wind speed • QuikScat surface wind Each center used their usual data selection and QC procedures, error statistics and observation operators NRL and GMAO assimilated SSM/I speeds but no Profiler winds; EC did the opposite

  9. Time series of forecast error norms Global domain: All assimilation times Jan 2007 NOGAPS GEOS-5 Forecast error on Background Trajectory, Forecast error on Analysis Trajectory, Difference of nonlinear forecast error norms Adjoint-based estimate ofglobal observation impact

  10.            Daily average observation impacts Global domain: 00+06 UTC assimilations Jan 2007 NOGAPS GEOS-5 FCST ERROR REDUCTION EC-MSGFS AMSU-A, Raob, Satwind and Aircraft have largest impact in all systems Smaller impact of Satwind in GEOS-5 than in other systems FCST ERROR REDUCTION

  11.  >30!  Impacts per-observation Global domain: 00+06 UTC assimilations Jan 2007 NOGAPS GEOS-5 FCST ERROR REDUCTION EC-MSGFS GEOS-5 has smaller impact per-ob, because more observations are assimilated (next slide) …recall TOTAL impacts are similar Very large impact per-ob for Ships in EC system is an outlier FCST ERROR REDUCTION

  12.            Observation Counts Global domain: 00+06 UTC assimilations Jan 2007 NOGAPS GEOS-5 EC-MSGFS GEOS-5 assimilates more obs overall, especially AMSU-A, Aircraft, QuikScat NOGAPS assimilates more Satwinds

  13. Fraction of obs that reduce forecast error Global domain: 00+06 UTC assimilations Jan 2007 NOGAPS GEOS-5 EC-MSGFS All observation types (except SSMI speeds in GEOS-5) are in the range of 50% to 54% beneficial

  14. Scatter of observation impact vs. innovation Global domain: 00 UTC assimilation 21 Jan 2007 GEOS-5 NOGAPS N18 AMSUA Ch 7 N18 AMSUA Ch 7 FCST ERROR INCREASE FCST ERROR REDUCTION Most total forecast error reduction comes from observations with moderate-size innovations – not from outliers with very large positive or negative innovations Raob T 300-700 hPa Raob T 300-700 hPa FCST ERROR INCREASE FCST ERROR REDUCTION

  15.               Observation impacts: AMSU-A channels 4 -11 Global domain: 00+06 UTC assimilations Jan 2007 NOGAPS GEOS-5 EC-MSGFS Large impact from channels5, 6 and 7 in all systems Small impact of channel 8 in NOGAPS; significant impact of channel 9 in EC-MSGFS

  16. Observation impacts: NOAA-18 channel 7 Global domain: 00+06 UTC assimilations Jan 2007 NOGAPS GEOS-5 Observations that produce large forecast error reductions Observations that produce forecast error increases in both models …land or ice surface contamination of radiance data?

  17. Summary of Comparison Results to Date • Comparison experiments for GMAO, NRL and EC systems completed for baseline set of observations • Despite differences in algorithms, RTMs and data handling, overall quantitative results similar for all systems; but details of impact differ (e.g., impact-per-ob, channels) • Largest impacts provided by AMSU-A and raobs (GMAO), AMSU-A and satwinds (NRL, EC); aircraft also has large impact in all systems • Only a small majority (50-55%) of assimilated observations improve the forecast…targeting ramifications? • Common problem areas with AMSU-A noted; handling of surface properties a likely cause • Future study to include other models(?) and more recent observation types…

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